similar to: How to use Lines function to draw the error bars?

Can someone please help with the error message below? thanks! Start: AIC=-Inf value ~ 1 + Core_CPI__ + GDP_change + Unemployment + housing + interest + S_P + d1 + d2 + d3 Error in if (any(ch)) { : missing value where TRUE/FALSE needed In addition: Warning message: attempting model selection on an essentially perfect fit is nonsense

My stepAIC function works for one set of data but not another....one set of data shows the steps of eliminating variables, versus another set of data doesn't throw away any variables. Can anyone please explain why? Thanks ______________________________________________________________________ The information transmitted, including any attachments, is intended only for the person or entity

See error message below: can someone please help with this? Thanks! Residuals: ALL 9 residuals are 0: no residual degrees of freedom! Residual standard error: NaN on 0 degrees of freedom Multiple R-squared: 1, Adjusted R-squared: NaN F-statistic: NaN on 8 and 0 DF, p-value: NA ______________________________________________________________________ The information transmitted,

Can someone please help with the below - thanks! Warning messages: 1: Not plotting observations with leverage one: 7 2: Not plotting observations with leverage one: 7 > print(qqPlot(fit),envelop=.95); Error in model.frame.default(formula = Y ~ X - 1, drop.unused.levels = TRUE) : variable lengths differ (found for 'X') In addition: Warning message: In matrix(yhat, n, reps) : data

Can someone please help with the error message below? Warning messages: 1: Not plotting observations with leverage one: 7 2: Not plotting observations with leverage one: 7 > print(qqPlot(fit),envelop=.95); Error in model.frame.default(formula = Y ~ X - 1, drop.unused.levels = TRUE) : variable lengths differ (found for 'X') In addition: Warning message: In matrix(yhat, n, reps) :

Dear R community, I'm trying to graphically illustrate my data with a worlmap. Unfortunately, my data is partly on country basis and partly on regional basis (e.g. certain African countries are aggregated to one region). I am using the package rwoldmap. The data on country basis can be mapped, but our defined regions cannot be identified in R. Therefore, all the countries in these

Dear All, We are currently working with very large datasets which even in R require a lot of calculation time. Is there an option of including a function/ tool or something alike which shows the remaining time of calculation? Just to see whether there is any progress or whether anything has crashed and to get a feeling whether we have to wait for 5 min or 30min... Any help is greatly

Dear All, at the moment I am using R for calculations of large databases. Unfortunately, R only manages to complete certain operations at some times, and not at others. I usually get the error message "cannot allocate vector of size XX" I am using the 64-bit version with Windows 7. While my computer has 8 RAM, I do have a feeling that R cannot use all of it. Searching online, I

Ran a bunch of variables in R and the final result of StepAIC is as below: Why are the first 5 variables kept in the stepwise result?? Are the last 4 variables finally chosen after Stepwise? Thanks Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.315e-01 2.687e-01 0.490 0.63611 Core_CPI__ 1.290e-02 7.496e-03 1.721 0.11927 GDP_change -3.482e-03 2.075e-03 -1.678 0.12767

For example, if coefficient's p-value is less than 0.1 I want the stepwise to automatically drop that variable. Can the stepAIC be customized to do that? SAS seems to be able to customized stepwise function with p-value or cooks'd. thanks! ______________________________________________________________________ The information transmitted, including any attachments, is intended only

Hi there, I'm trying to prep some data for a persp() surface plot representing the predictions from a regression with two inddependent variables. The regression model "m3" has an intercept, 2 linear terms, and 2 squared terms. The coefficients are given by coef(m3). My approach to generating the predictions for a range of each of my IVs, "s" and "d" was

Hi all, I wonder why there is so little software for carpet plots (german: Rasterdiagramm) (Three dimensional plot (x, y, z), the 3rd dimension (z) symbolized by colourgradients). Besides from one or the other non free software I only found an OpenOffice macro, a combination of Gnuplot and Excel (an Excel macro calling gnuplot) (http://www.johannes-hopf.de/2009/12/carpet-plot-version-1-3/9 and

Hi all:I found some great demonstrations of interactive presentation graphics generated in R with the SVGAnnotation package, here:http://www.omegahat.org/SVGAnnotation/http://www.omegahat.org/SVGAnnotation/SVGAnnotationPaper/SVGAnnotationPaper.htmlI tried to install the package available at that website (it's not on CRAN) and am getting some pretty uninformative errors (see below). My best

----- Original Message ----- From: Karl Heyes <karl@pts.tele2.co.uk> To: <icecast@xiph.org> Sent: Friday, January 31, 2003 5:29 PM Subject: Re: [icecast] Re: Compiling ices <p>> On Fri, 2003-01-31 at 06:39, Marc Remijn wrote: > ... > > > Kernel output: > > > > invalid operand: 0000 > > CPU: 0 > > EIP: 0010:[<d08e25b1>]

Good day everyone, Can anyone suggest an effective method to solve the following problem: I have 2 dataframes D1 and D2 as follows: D1: dates ws wc pwc 2005-10-19:12:00 10.8 80 81 2005-10-20:12:00 12.3 5 15 2005-10-21:15:00 12.3 3 15 2005-10-22:15:00 11.3 13 95 2005-10-23:12:00 12.3 13 2 2005-10-24:15:00 10.3 2 95 2005-10-25:15:00 10.3 2 2 D2:

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

For a set of data showing seasonality (related to the 4th quarter), ncv test in R shows p-value of 0.008 which rejects the null hypothesis of constant-variance. How to apply White's standard error in R? thanks ______________________________________________________________________ The information transmitted, including any attachments, is intended only for the person or entity to

Can someone please help with the error message below -- thanks! Warning messages: 1: Not plotting observations with leverage one: 7 2: Not plotting observations with leverage one: 7 > print(qqPlot(fit),envelop=.95); Error in model.frame.default(formula = Y ~ X - 1, drop.unused.levels = TRUE) : variable lengths differ (found for 'X') In addition: Warning message: In matrix(yhat, n,

For a set of data showing seasonality (related to the 4th quarter), ncv test shows p-value of 0.008 which rejects the null hypothesis of constant-variance. Currently a linear LM relationship is being applied to the data. Should white's error be used to correct the non-constant variance? Are the p-values of the coefficients unreliable? thanks

Hi everyone, I am trying to build a table putting standard errors horizontally. I haven't been able to do it. library(memisc) berkeley <- aggregate(Table(Admit,Freq)~.,data=UCBAdmissions) berk0 <- glm(cbind(Admitted,Rejected)~1,data=berkeley,family="binomial") berk1 <- glm(cbind(Admitted,Rejected)~Gender,data=berkeley,family="binomial") berk2 <-